Generalizable Time-Series Models.
There are fewer studies about model robustness to distribution shift on time-series data [1]}. AdaRNN proposes to characterize the temporal distribution shift of signals and reduce the mismatch with an RNN [2]}. Godahewa et al. provided a dataset archive for general time-series forecasting algorithms evaluation [3]}.
As for generalizable sensor-based human behavior modeling, some researchers have explored short-term human action recognition [4]}, [5]}, [6]}.
However, these studies primarily rely on data collected in a controlled setting for a short period (minutes to hours) [7]}, [8]}. There is little research focusing on in-the-wild longitudinal human behavior sensor data (months to years) that contains diverse and variable contexts of daily livings.
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